Risk Nomogram Research Articles

Factors influencing agitation during anesthesia recovery after laparoscopic hernia repair under total inhalation combined with caudal block anesthesia

BACKGROUND Laparoscopic hernia repair is a minimally invasive surgery, but patients may experience emergence agitation (EA) during the post-anesthesia recovery period, which can increase pain and lead to complications such as wound reopening and bleeding. There is limited research on the risk factors for this agitation, and few effective tools exist to predict it. Therefore, by integrating clinical data, we have developed nomograms and random forest predictive models to help clinicians predict and potentially prevent EA. AIM To establish a risk nomogram prediction model for EA in patients undergoing laparoscopic hernia surgery under total inhalation combined with sacral block anesthesia. METHODS Based on the clinical information of 300 patients who underwent laparoscopic hernia surgery in the Nanning Tenth People’s Hospital, Guangxi, from January 2020 to June 2023, the patients were divided into two groups according to their sedation-agitation scale score, i.e. , the EA group (≥ 5 points) and the non-EA group (≤ 4 points), during anesthesia recovery. Least absolute shrinkage and selection operator regression was used to select the key features that predict EA, and incorporating them into logistic regression analysis to obtain potential predictive factors and establish EA nomogram and random forest risk prediction models through R software. RESULTS Out of the 300 patients, 72 had agitation during anesthesia recovery, with an incidence of 24.0%. American Society of Anesthesiologists classification, preoperative anxiety, solid food fasting time, clear liquid fasting time, indwelling catheter, and pain level upon awakening are key predictors of EA in patients undergoing laparoscopic hernia surgery with total intravenous anesthesia and caudal block anesthesia. The nomogram predicts EA with an area under the receiver operating characteristic curve (AUC) of 0.947, a sensitivity of 0.917, and a specificity of 0.877, whereas the random forest model has an AUC of 0.923, a sensitivity of 0.912, and a specificity of 0.877. Delong’s test shows no significant difference in AUC between the two models. Clinical decision curve analysis indicates that both models have good net benefits in predicting EA, with the nomogram effective within the threshold of 0.02 to 0.96 and the random forest model within 0.03 to 0.90. In the external model validation of 50 cases of laparoscopic hernia surgery, both models predicted EA. The nomogram model had a sensitivity of 83.33%, specificity of 86.84%, and accuracy of 86.00%, while the random forest model had a sensitivity of 75.00%, specificity of 78.95%, and accuracy of 78.00%, suggesting that the nomogram model performs better in predicting EA. CONCLUSION Independent predictors of EA in patients undergoing laparoscopic hernia repair with total intravenous anesthesia combined with caudal block include American Society of Anesthesiologists classification, preoperative anxiety, duration of solid food fasting, duration of clear liquid fasting, presence of an indwelling catheter, and pain level upon waking. The nomogram and random forest models based on these factors can help tailor clinical decisions in the future.

Construction and validation of a nomogram prediction model for the risk of new-onset atrial fibrillation following percutaneous coronary intervention in acute myocardial infarction patients.

The objective of this study was to investigate risk factors for new-onset atrial fibrillation (NOAF) post-percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI), aiming to develop a predictive nomogram for NOAF risk. A retrospective cohort study involving 397 AMI patients who underwent PCI at a tertiary hospital in Anhui, China, from January 2021 to July 2022 was performed. Patients were divided into NOAF (n = 63) and non-NOAF (n = 334) groups based on post-PCI outcomes. Clinical data were extracted from the hospital information system (HIS) and analyzed using univariate and multivariate logistic regression to identify independent risk factors. A nomogram was generated utilizing R software (version 3.6.1), with its performance evaluated through receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Bootstrap resampling. Independent risk factors for NOAF included age, left atrial diameter (LAD), Gensini score, N-terminal pro-B-type natriuretic peptide (NT-proBNP), alanine transaminase (ALT), low-density lipoprotein cholesterol (LDL-C), left ventricular end-systolic diameter (LVESD), and ventricular rate (P < 0.05). The nomogram's ROC curve demonstrated an area under the curve (AUC) of 0.925 (95% CI: 0.887-0.963), supported by a Bootstrap-verified AUC of 0.924 (95% CI: 0.883-0.954), reflecting strong discriminative capability. The calibration curve indicated a mean absolute error (MAE) of 0.031 and 0.017 prior to and following Bootstrap verification, respectively, signifying robust calibration. The DCA curve illustrated that the nomogram offered optimal clinical net benefit for patients with a threshold probability of NOAF ranging from 0.01 to 0.99. The nomogram developed from independent risk factors for NOAF exhibits significant predictive accuracy and clinical relevance for evaluating the risk of NOAF in AMI patients following PCI, thereby enabling the identification of high-risk individuals for targeted interventions.

Predicting thyroid involvement in primary Sjögren’s syndrome: development and validation of a predictive nomogram

IntroductionPatients with Primary Sjögren’s syndrome (pSS) are at a higher risk of thyroid disorders than the general population. This retrospective analysis of 202 patients with pSS was conducted to uncover risk factors associated with thyroid involvement and to create a predictive model for this condition.MethodsWe analyzed 202 patients with pSS from Guang’anmen Hospital, China Academy of Chinese Medical Sciences, with 105 cases of thyroid involvement and 97 without. The Least Absolute Shrinkage and Selection Operator method was used to identify key variables for our risk model. These variables were then subjected to multivariate logistic regression to develop the model. The accuracy of the model was assessed through the C-index, receiver operating characteristic curves, calibration plots, and decision curve analysis, with internal validation via bootstrapping.ResultsHigh-sensitivity C-reactive protein (HCRP), pulmonary disease, pharyngeal dryness, forgetfulness, night sweats, hyperuricemia, nasal dryness, anxiety, Ro52, and aspartate aminotransferase (AST) were incorporated into the nomogram. The model showed robust discrimination and calibration abilities. Decision curve analysis indicated the clinical utility of our nomogram in intervening on the probability thresholds of thyroid disease.ConclusionBy integrating HCRP, pulmonary disease, pharyngeal dryness, forgetfulness, night sweats, hyperuricemia, nasal dryness, anxiety, Ro52, and AST, our thyroid risk nomogram can predict the risk of thyroid involvement in patients with pSS, aiding in more informed treatment strategies.

Establishment and validation of a predictive model for lower extremity deep vein thrombosis in patients with traumatic pelvic fractures

BackgroundPatients with traumatic pelvic fracture (TPF) are at high risk for developing deep vein thrombosis (DVT). However, there is still no unified standard on how to distinguish high-risk groups for DVT in patients with TPF and how to accurately use anticoagulants at present.ObjectivesThis observational study aimed to establish a DVT risk nomogram score (DRNS) model for TPF patients, and to explore the value of the DRNS model as a clinical guideline in the prevention of DVT with low molecular weight heparin (LMWH).MethodsIndependent risk factors of lower extremity DVT were screened through Lasso regression and logistic regression. A DRNS model was established per this.ResultsThe independent risk factors of DVT included combined femoral fractures, age ≥ 40 years old, BMI (body mass index) ≥ 24 kg/m2, ISS score, fibrinogen concentration, and the minimum concentration of ionized calcium within 48 h after admission. The optimal cutoff value for DRNS was 78.5. In the low-risk population of DVT (DRNS < 78.5), there was no statistical significance of variation about the incidence of DVT progression between the LMWH once a day (qd) group and the LMWH once every 12 h (q12h) group, with P = 0.323. In the high-risk population of DVT (DRNS ≥ 78.5), the incidence of DVT progression in the LMWH qd group was significantly higher than that in the LMWH q12h group, with P = 0.002.ConclusionsThe DRNS model based on independent risk factors of DVT could stratify the risk of DVT for TPF patients, and it was able to provide more precise DVT drug prevention plans for clinicians.

Development and validation of the competing risk nomogram and risk classification system for predicting cancer-specific mortality in patients with cervical adenosquamous carcinoma treated via radical hysterectomy.

In this study, we established and validated a competing risk nomogram for predicting the cumulative incidence of cervical adenosquamous carcinoma (ASC)-specific death in patients undergoing radical hysterectomy. Patients diagnosed with ASC between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function (CIF) for various variables influencing ASC-specific mortality was computed. A Fine-Gray competing risk model was used to identify independent predictors, formulating a competing risk nomogram. A multivariate Cox proportional hazards model was also applied for comparative analysis. The performance of the nomogram was assessed using metrics such as the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A corresponding risk classification system was constructed based on nomogram-derived scores. Factors such as advanced age, racial background (Black race), higher tumor grade, increased tumor size, advanced TNM stage, and receipt of radiotherapy without chemotherapy were found to be positively associated with elevated ASC-specific mortality. Additionally, age, T stage, M stage, and chemotherapy were identified as independent predictors correlated with ASC-specific mortality. The established nomogram exhibited accurate discriminatory capabilities and superior net benefits compared to the traditional TNM staging system. Additionally, the high-risk group consistently demonstrated higher probabilities of ASC-specific death in both the training and validation sets. The developed nomogram proficiently quantified the incidence of ASC-specific death in patients subjected to radical hysterectomy for ASC. This tool could help clinicians in formulating personalized treatment strategies and devising follow-up protocols.

Risk nomogram for papillary thyroid microcarcinoma with central lymph node metastasis and postoperative thyroid function follow-up.

The treatment for papillary thyroid microcarcinoma (PTMC) is controversial. Central lymph node metastasis (CLNM) is one of the main predictors of recurrence and survival, accurate preoperative identification of CLNM is essential for surgical protocol establishment for PTMC. The objective of this study was to establish a nomogram to predict the possibility of CLNM in PTMC patients. A total of 3023 PTMC patients were randomly divided into two groups by a ratio of 7 to 3, the training group (n = 2116) and validation group (n = 907). The LASSO regression model and multivariate logistic regression analysis were performed to examine risk factors associated with CLNM. A nomogram for predicting CLNM was established and internally validated. Meanwhile, we follow-up the serum thyroid function FT3, FT4, TSH, Tg, TGAb and TPOAb in 789 PTMC patients for 4 years after surgery and compared the differences between the CLNM (+) and CLNM (-) groups, respectively. The LASSO regression model and multivariate logistic regression analysis showed that younger age, lower BMI, being male, location in the lower pole, calcification, 1 ≥ diameter ≥ 0.5 cm, multifocality lesions, extra thyroidal extension (ETE), enlargement of central lymph node (ECLN), lateral lymph node metastasis (LLNM) and higher carcinoembryonic antigen were the ultimate risk factors for determining CLNM. A nomogram for predicting CLNM was constructed based on the influencing factors and internally validated. By establishing the prediction model, the AUC of CLNM in the training and validation groups were 0.73 (95% CI, 0.70-0.76) and 0.75 (95% CI, 0.71-0.79) respectively. Results of the DCA showed that the model is clinically useful when deciding on intervention in the most range of the threshold probability. A 4-year follow-up of thyroid function showed that FT3 and FT4 remained at stable levels after 3 months postoperative and were higher in the CLNM (+) group than in the CLNM (-) group. Hypothyroidism appeared predominantly within 3 months after surgery. The overall incidence of the CLNM (+) group and CLNM (-) groups were 16.46% and 12.04%, respectively. The nomogram model constructed in this study has a good predictive effect on CLNM in PTMC patients and provides a reasonable reference for clinical treatment.

Predictive models for cholesterol crystals and plaque vulnerability in acute myocardial infarction: Insights from an optical coherence tomography study

BackgroundCholesterol crystals (CCs) are recognized as a risk factor for vulnerable atherosclerotic plaque rupture (PR) and major adverse cardiovascular events. However, their predictive factors and association with plaque vulnerability in patients with acute myocardial infarction (AMI) remain insufficiently explored. Therefore, This study aims to investigate the association between CCs and plaque vulnerability in culprit lesions of AMI patients, identify the factors influencing CCs formation, and develop a predictive model for CCs. MethodsA total of 431 culprit lesions from AMI patients who underwent pre-intervention optical coherence tomography (OCT) imaging were analyzed. Patients were divided into groups based on the presence or absence of CCs and PR. The relationship between CCs and plaque vulnerability was evaluated. A risk nomogram for predicting CCs was developed using the least absolute shrinkage and selection operator and logistic regression analysis. ResultsCCs were identified in 64.5 % of patients with AMI. The presence of CCs was associated with a higher prevalence of vulnerable plaque features, such as thin-cap fibroatheroma (TCFA), PR, macrophage infiltration, neovascularization, calcification, and thrombus, compared to patients without CCs. The CCs model demonstrated an area under the curve (AUC) of 0.676 for predicting PR. Incorporating CCs into the TCFA model (AUC = 0.656) significantly enhanced predictive accuracy, with a net reclassification improvement index of 0.462 (95 % confidence interval [CI]: 0.263–0.661, p < 0.001) and an integrated discrimination improvement index of 0.031 (95 % CI: 0.013–0.048, p = 0.001). Multivariate regression analysis identified the atherogenic index of plasma (odds ratio [OR] = 2.417), TCFA (OR = 1.759), macrophage infiltration (OR = 3.863), neovascularization (OR = 2.697), calcification (OR = 1.860), and thrombus (OR = 2.430) as independent risk factors for CCs formation. The comprehensive model incorporating these factors exhibited reasonable discriminatory ability, with an AUC of 0.766 (95 % CI: 0.717–0.815) in the training set and 0.753 (95 % CI: 0.704–0.802) in the internal validation set, reflecting good calibration. Decision curve analysis suggested that the model has potential clinical utility within a threshold probability range of approximately 18 % to 85 %. ConclusionsCCs were associated with plaque vulnerability in the culprit lesions of AMI patients. Additionally, this study identified key factors influencing CCs formation and developed a predictive model with potential clinical applicability.

Individualized evaluation of risk and prognosis in uterine leiomyosarcoma patients with synchronous distant metastases: a real-world retrospective study.

Uterine leiomyosarcoma (uLMS) accounts for roughly 70% of all uterine sarcomas, with recurrence and mortality rates notably higher than those of other uterine tumors. The prognosis of uLMS patients who have distant metastases remains poor. The objective of this study was to determine independent risk variables related to distant metastases in patients with uLMS and prognostic factors for those with distant metastases. Subsequently, two practical nomograms were developed and validated to assess the probability of distant metastases and predict survival outcomes for these with distant metastases, respectively. A real-world retrospective study was carried out using data from patients diagnosed with primary uLMS in the Surveillance, Epidemiology, and End Results (SEER) database spanning the years 2010 to 2015. Univariate andmultivariate logistic regression analyses were utilized to identify clinicopathological characteristics related to the risk of distant metastases, while univariate and multivariate Cox regressions were employed to determine prognostic factors. Then, a risk nomogram incorporating independent risk variables and a prognostic nomogram integrating independent prognostic factors were established in the training cohort and validated for accuracy in the validation cohort, respectively. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and calibration curves were utilized to measure the accuracy of nomograms, while decision curve analysis (DCA) curves were employed to assess their clinical benefit capacity. Based on the median total point derived from the prognostic nomogram, patients were stratified into high- and low-risk groups. The differentiation ability of the prognostic nomogram was evaluated using Kaplan-Meier survival analysis with the log-rank test. The study encompassed 1,362 patients diagnosed with uLMS, among whom 337 cases (24.7%) manifested synchronous distant metastases at the initial diagnosis. Univariate and multivariate logistic regression analyses identified race, histological grade, T stage, N stage, tumor size, surgery, and chemotherapy as independent risk factors for distant metastases in uLMS patients. The outcomes of both univariate and multivariate Cox analyses indicated that surgery and chemotherapy emerged as independent protective factors for prognosis in uLMS patients with distant metastases, whereas higher histological grade and T stage were identified as independent risk factors. The risk nomogram incorporating independent risk variables and the prognostic nomogram integrating independent prognostic factors could respectively predict the risk of metastases and the prognosis very effectively in both training and validation cohorts. In summary, we developed the novel well-validated risk nomogram to precisely assess the probability of metastases in uLMS patients and prognostic nomogram to predict the prognosis of those with distant metastases, providing decision-making guidance for tailoring individualized clinical management of these patients.

A nomogram to predict cancer-specific mortality in adult patients with malignant meningioma: a competing risk analysis

BackgroundComprehensive investigations of the prognosis factors and treatment strategies with adjustment of competing causes of death for patients with malignant meningioma (MM) is still lacking.Patient and methodThe surveillance, Epidemiology, and End Results (SEER) database were used to include adult patients with this rare disease between 2004 and 2018. The probability of MM-caused mortality (MMCM) and non-MM-caused mortality (non-MMCM) were presented by cumulative incidence function curves. Then, the association between variates with non-MMCM was evaluated by the cox proportional hazard model, and the prognostic factors of MMCM were identified by Fine-Gray competing risk regression model. Furthermore, a nomogram was developed to predict the 1-year, 2-year, and 5-year MMCM and the performance was tested by a time-dependent area under the receiver operating characteristic (ROC) curve and calibration.Result577 patients were included, with a median age of 62 (18–100) years old and a median overall survival time of 36 (0–176) months. The percentage of non-MMCM was 15.4% (n = 89) in the entire population and 21.7% (n = 54) in elderly patients. The multivariable Cox proportional hazard regression model revealed that older age and other tumor(s) before or after MM had an independently significant association with higher non-MMCM. After adjustment of competing causes of death, the multivariable Fine-gray regression model identified age group ≥ 65 year, tumor size > 5.3 cm, recurrent MM, and histologic type 9530/3 (Meningioma, malignant) had an independently significant association with higher MMCM. Compared with gross total (GTR) of tumor, subtotal resection of tumor (HR 1.66, 95%CI 1.08–2.56, P = 0.02), partial resection of lobe (HR 2.26, 95%CI 1.32–3.87, P = 0.003), and gross total resection of lobe (HR 1.69, 95%CI 1.12–2.51, P = 0.01) had an independently significant association with higher MMCM.ConclusionThe competing risk nomogram including age group, tumor size, initial status, histologic type, and extent of resection is discriminative and clinically useful. This study emphasized the importance of the GTR of tumor in the treatment of MM patients, which had a significantly lower incidence of MMCM compared with biopsy, STR of tumor, partial resection of lobe, and GTR of lobe.

Construction of a Prognostic Nomogram for Major Amputation Within 30 Days Postrevascularization in Patients With Acute Lower Limb Ischemia Based on 2D Perfusion Parameters.

The purpose of the study is to develop a prediction model for major amputation (MA) within 30 days after arterial revascularization in patients with acute lower limb ischemia (ALLI) using 2-dimensional (2D) perfusion imaging parameters. A retrospective study was performed in ALLI patients undergoing arterial revascularization between October 2015 and May 2022. Patients were randomly assigned into training and validation cohorts in a ratio of 7:3. Variables were selected using univariate and multivariate logistic regression. A nomogram for the MA risk within 30 days after arterial revascularization in ALLI patients was created. Its discrimination, calibration, and clinical effectiveness were reported. A total of 310 ALLI patients (326 limbs) were included. The MA rate within 30 days after arterial revascularization was 11.6%. Skin speckle, myoglobin, and time-to-peak were independent risk factors, while atrial fibrillation was a protective factor (all p<0.05). The nomogram predicted 30-day MA with satisfactory discriminative ability. The integrated discrimination improvement was 0.279 and 0.379 for the training and validation cohorts, respectively (both p<0.001). Calibration curves were close to the standard curve. The decision curve analysis demonstrated net benefits. This 2D perfusion imaging parameter-based nomogram could accurately predict the risk of MA within 30 days postrevascularization in ALLI patients. This study introduces a novel nomogram based on 2-dimensional (2D) perfusion imaging that can significantly advance the prognosis prediction in ALLI patients. By calculating the risk of major amputation within 30 days postrevascularization, this nomogram offers an accurate predictive tool and can lead to more informed decision-making on patient management. The innovative aspect of this research lies in its utilization of 2D perfusion parameters, a novel approach that enhances risk assessment accuracy in ALLI patients. This nomogram represents a significant step toward risk stratification and can guide future research for appropriate management on ALLI patients with different risk profiles.

Development and Validation of a Risk Predictive Model for Small Intracranial Aneurysms in Adults Over a Five-Year Period.

Objective The optimal management of a small intracranial aneurysm (sIA) remains a challenge due to the lack of a size-specific risk predictive model for aneurysm rupture. We aimed to develop and validate a nomogram-based risk predictive model for sIA. Methods A total of 382 patients harboring 215 ruptured and 167 unruptured small intracranial aneurysms (uSIAs) (≤ 7 mm) were recruited and divided into training and validation cohorts. Risk factors for the construction of a nomogram were selected from clinical and aneurysmal features by least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. The nomogram for risk of rupture was evaluated in both the training and validation cohorts for discrimination, calibration, and clinical usefulness. Results Hyperlipidemia (odds ratio (OR)=2.74, 95% confidence interval (CI)=1.322~5.956, P=0.008), the presence of a daughter dome (OR=3.068, 95%CI=1.311~7.598, P=0.012), larger size-to-neck ratio (SN) (OR=1.807, 95%CI=1.131~3.063, P=0.021) and size ratio (SR) (OR=2.221, 95%CI=1.262~4.025, P=0.007) were selected as independent risk factors for sIA rupture and used for construction of nomogram. Internal validation by bootstrap sampling showed the Concordance index (C index) of 0.756 for the nomogram. The calibration by the Hosmer-Lemeshow test showed a P value of 0.847, indicating the model was well-fitted. Additionally, decision curve analysis (DCA) demonstrated that the predictive model has good clinical usefulness, providing net benefits across a range of threshold probabilities, thus supporting its application in clinical decision-making. Conclusion The risk prediction model can reliably predict the risk of sIA rupture, which may provide an important reference for optimizing the therapeutic strategy.

Development and validation of competing risk nomograms for predicting cancer‑specific mortality in non-metastatic patients with non‑muscle invasive urothelial bladder cancer

We aimed to assess the cumulative incidences of cancer-specific mortality (CSM) in non-metastatic patients with non‑muscle invasive urothelial bladder cancer (NMIUBC) and establish competing risk nomograms to predict CSM. Patient data was sourced from the Surveillance, Epidemiology, and End Results database, as well as the electronic medical record system in our institution to form the external validation cohort. Sub-distribution proportional hazards model was utilized to determine independent risk factors influencing CSM in non-metastatic NMIUBC patients. Competitive risk nomograms were constructed to predict 3-year, 5-year, and 8-year cancer-specific survival (CSS) in all patients group, TURBT group and cystectomy group, respectively. The discrimination and accuracy of the model were validated through the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves. Decision curve analysis (DCA) and a risk stratification system was employed to evaluate the clinical utility of the model. Race, age, marital status, surgery in other sites, tumor size, histological type, histological grade, T stage and N stage were identified as independent risk factors to predict CSS in all patients group. The C-index for 3-year CSS was 0.771, 0.770 and 0.846 in the training, testing and external validation sets, respectively. The ROC curves showed well discrimination and the calibration plots were well fitted and consistent. Moreover, DCA demonstrated well clinical effectiveness. Altogether, the competing risk nomogram displayed excellent discrimination and accuracy for predicting CSS in non-metastatic NMIUBC patients, which can be applied in clinical practice to help tailor treatment plans and make clinical decisions.

Risk prediction and prognostic analysis of post-implantation syndrome after thoracic endovascular aortic repair

This study aimed to establish a predictive model for the risk of post-thoracic endovascular aortic repair (TEVAR) post-implantation syndrome (PIS) in type B aortic dissection (TBAD) patients, assisting clinical physicians in early risk stratification and decision management for high-risk PIS patients. This study retrospectively analyzed the clinical data of 547 consecutive TBAD patients who underwent TEVAR treatment at our hospital. Feature variables were selected through LASSO regression and logistic regression analysis to construct a nomogram predictive model, and the model's performance was evaluated. The optimal cutoff value for the PIS risk nomogram score was calculated through receiver operating characteristic (ROC) curve analysis, further dividing patients into high-risk group (HRG) and low-risk group (LRG), and comparing the short to midterm postoperative outcomes between the two groups. In the end, a total of 158 cases (28.9%) experienced PIS. Through LASSO regression analysis and multivariable logistic regression analysis, variables including age, emergency surgery, operative time, contrast medium volume, and number of main prosthesis stents were selected to construct the nomogram predictive model. The model achieved an area under the curve (AUC) of 0.86 in the training set and 0.82 in the test set. Results from calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated that the predictive model exhibited good performance and clinical utility. Furthermore, after comparing the postoperative outcomes of HRG and LRG patients, we found that the incidence of postoperative PIS significantly increased in HRG patients. The duration of ICU stay and mechanical assistance time was prolonged, and the incidence of postoperative type II entry flow and acute kidney injury (AKI) was higher. The risk of aortic-related adverse events (ARAEs) and major adverse events (MAEs) at the first and twelfth months of follow-up also significantly increased. However, there was no significant difference in the mortality rate during hospitalization. This study established a nomogram model for predicting the risk of PIS in patients with TBAD undergoing TEVAR. It serves as a practical tool to assist clinicians in early risk stratification and decision-making management for patients.

Development and validation of a predictive model for PACU hypotension in elderly patients undergoing sedated gastrointestinal endoscopy

BackgroundHypotension, characterized by abnormally low blood pressure, is a frequently observed adverse event in sedated gastrointestinal endoscopy procedures. Although the examination time is typically short, hypotension during and after gastroscopy procedures is frequently overlooked or remains undetected. This study aimed to construct a risk nomogram for post-anesthesia care unit (PACU) hypotension in elderly patients undergoing sedated gastrointestinal endoscopy.MethodsThis study involved 2919 elderly patients who underwent sedated gastrointestinal endoscopy. A preoperative questionnaire was used to collect data on patient characteristics; intraoperative medication use and adverse events were also recorded. The primary objective of the study was to evaluate the risk of PACU hypotension in these patients. To achieve this, the least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection, involving cyclic coordinate descent with tenfold cross-validation. Subsequently, multivariable logistic regression analysis was applied to build a predictive model using the selected predictors from the LASSO regression. A nomogram was visually developed based on these variables. To validate the model, a calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used. Additionally, external validation was conducted to further assess the model’s performance.ResultsThe LASSO regression analysis identified predictors associated with an increased risk of adverse events during surgery: age, duration of preoperative water abstinence, intraoperative mean arterial pressure (MAP) <65 mmHg, decreased systolic blood pressure (SBP), and use of norepinephrine (NE). The constructed model based on these predictors demonstrated moderate predictive ability, with an area under the ROC curve of 0.710 in the training set and 0.778 in the validation set. The DCA indicated that the nomogram had clinical applicability when the risk threshold ranged between 20 and 82%, which was subsequently confirmed in the external validation with a range of 18–92%.ConclusionIncorporating factors such as age, duration of preoperative water abstinence, intraoperative MAP <65 mmHg, decreased SBP, and use of NE in the risk nomogram increased its usefulness for predicting PACU hypotension risk in elderly patient undergoing sedated gastrointestinal endoscopy.

Development and validation of a nomogram to predict mortality of patients with DIC in ICU.

Disseminated intravascular coagulation (DIC) is a devastating condition, which always cause poor outcome of critically ill patients in intensive care unit. Studies concerning short-term mortality prediction in DIC patients is scarce. This study aimed to identify risk factors contributing to DIC mortality and construct a predictive nomogram. A total of 676 overt DIC patients were included. A Cox proportional hazards regression model was developed based on covariates identified using least absolute shrinkage and selection operator (LASSO) regression. The prediction performance was independently evaluated in the MIMIC-III and MIMIC-IV Clinical Database, as well as the 908th Hospital Database (908thH). Model performance was independently assessed using MIMIC-III, MIMIC-IV, and the 908th Hospital Clinical Database. The Cox model incorporated variables identified by Lasso regression including heart failure, sepsis, height, SBP, lactate levels, HCT, PLT, INR, AST, and norepinephrine use. The model effectively stratified patients into different mortality risk groups, with a C-index of >0.65 across the MIMIC-III, MIMIC-IV, and 908th Hospital databases. The calibration curves of the model at 7 and 28 days demonstrated that the prediction performance was good. And then, a nomogram was developed to facilitate result visualization. Decision curve analysis indicated superior net benefits of the nomogram. This study provides a predictive nomogram for short-term overt DIC mortality risk based on a Lasso-Cox regression model, offering individualized and reliable mortality risk predictions.

A risk score for predicting in-stent restenosis in patients with premature acute myocardial infarction undergoing percutaneous coronary intervention with drug-eluting stent

BackgroundThis study aimed at developing and validating a risk score to predict in-stent restenosis (ISR) in patients with premature acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) with drug-eluting stent (DES). MethodsThis was a two-center retrospective study. A total of 2185 patients firstly diagnosed with premature AMI (age ≥18 years and <55 years in men, <65 years in women) from Xinjiang cohort were retrospectively analyzed. After filtering by exclusion criteria, patients were randomly divided into training cohort (n = 434) and internal validation cohort (n = 186) at a 7:3 ratio. Several candidate variables associated with ISR in the training cohort were assessed by the least absolute shrinkage and selection operator and logistic regression analysis. The ISR risk nomogram score based on the superior predictors was finally developed, and then validated in the internal validation cohort and in an independent Chengdu external validation cohort (n = 192). The higher total nomogram score, the greater the ISR risk. ResultsThe eight variables in the final risk nomogram score, cardiovascular-kidney-metabolic (CKM) score included age, diabetes mellitus (DM), body mass index (BMI), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDLC), estimated glomerular filtration rate (eGFR), stent in left anterior descending coronary artery, minimum stent diameter <3 mm. The areas under the curve (AUC) and C-statistics [training cohort: 0.834 (95%CI: 0.787 to 0.882); internal validation cohort: 0.852 (95%CI: 0.784 to 0.921); Chengdu external validation cohort: 0.787 (95%CI: 0.692 to 0.882), respectively)] demonstrated the good discrimination of the CKM score. The Hosmer-Lemeshow test (χ2 = 7.86, P = 0.448; χ2 = 5.17, P = 0.740; χ2 = 6.35, P = 0.608, respectively) and the calibration curve confirmed the good calibration of the CKM score. Decision curve analysis (DCA) testified the clinical net benefit of the CKM score in the training and validation cohort. ConclusionThis study provided a well-developed and validated risk nomogram score, the CKM score to predict ISR in patients with premature AMI undergoing PCI with DES. Given that these variables are readily available and practical, the CKM score should be widely adopted for individualized assessment and management of premature AMI.

Development and validation of a novel prediction model for Carbapenem-resistant organism infection in a large-scale hospitalized patients

Carbapenem-resistant organism (CRO) are defined as gram-negative bacteria. The lack of safe and effective antibiotics has led to an increase in incidence rate. The purpose of this study is to establish and determine a risk nomogram to predict CRO infection in hospitalized patients. Hospitalized patients’ information were collected from the electronic medical record system of hospital between January 2019 and December 2022. Based on the inclusion and exclusion criteria, we identified 131390 inpatients who met the criteria for this study. For the training cohort, the area under the curves (AUC) for predicting the CRO infection was 0.935. For the validation cohort, the AUC for predicting the CRO infection was 0.937. We have developed the first novel nomogram to predict CRO infection in hospitalized patients, which is reliable and high-performance. The nomogram performs well among hospitalized patients and has good predictive ability.

Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

Patients diagnosed with hepatocellular carcinoma (HCC) often present with multimorbidity, significantly contributing to adverse outcomes, particularly in-hospital mortality. This study aimed to develop a predictive nomogram to assess the impact of comorbidities on in-hospital mortality risk in HCC patients undergoing palliative locoregional therapy. We retrospectively analyzed data from 345 hospitalized HCC patients who underwent palliative locoregional therapy between January 2015 and December 2022. The nomogram was constructed using independent risk factors such as length of stay (LOS), hepatitis B virus (HBV) infection, hypertension, chronic obstructive pulmonary disease (COPD), anemia, thrombocytopenia, liver cirrhosis, hepatic encephalopathy (HE), N stage, and microvascular invasion. The model demonstrated high predictive accuracy with an AUC of 0.908 (95% CI: 0.859–0.956) for the overall dataset, 0.926 (95% CI: 0.883–0.968) for the training set, and 0.862 (95% CI: 0.728–0.994) for the validation set. Calibration curves indicated a strong correlation between predicted and observed outcomes, validated by statistical tests. Decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility in predicting in-hospital mortality. This nomogram offers a practical tool for personalized risk assessment in HCC patients undergoing palliative locoregional therapy, facilitating informed clinical decision-making and improving patient management.

Development and application of a risk nomogram for the prediction of risk of carbapenem-resistant Acinetobacter baumannii infections in neuro-intensive care unit: a mixed method study

ObjectiveThis study aimed to develop and apply a nomogram with good accuracy to predict the risk of CRAB infections in neuro-critically ill patients. In addition, the difficulties and expectations of application such a tool in clinical practice was investigated.MethodsA mixed methods sequential explanatory study design was utilized. We first conducted a retrospective study to identify the risk factors for the development of CRAB infections in neuro-critically ill patients; and further develop and validate a nomogram predictive model. Then, based on the developed predictive tool, medical staff in the neuro-ICU were received an in-depth interview to investigate their opinions and barriers in using the prediction tool during clinical practice. The model development and validation is carried out by R. The transcripts of the interviews were analyzed by Maxqda.ResultsIn our cohort, the occurrence of CRAB infections was 8.63% (47/544). Multivariate regression analysis showed that the length of neuro-ICU stay, male, diabetes, low red blood cell (RBC) count, high levels of procalcitonin (PCT), and number of antibiotics ≥ 2 were independent risk factors for CRAB infections in neuro-ICU patients. Our nomogram model demonstrated a good calibration and discrimination in both training and validation sets, with AUC values of 0.816 and 0.875. Additionally, the model demonstrated good clinical utility. The significant barriers identified in the interview include “skepticism about the accuracy of the model”, “delay in early prediction by the indicator of length of neuro-ICU stay”, and “lack of a proper protocol for clinical application”.ConclusionsWe established and validated a nomogram incorporating six easily accessed indicators during clinical practice (the length of neuro-ICU stay, male, diabetes, RBC, PCT level, and the number of antibiotics used) to predict the risk of CRAB infections in neuro-ICU patients. Medical staff are generally interested in using the tool to predict the risk of CRAB, however delivering clinical prediction tools in routine clinical practice remains challenging.

Comment on "Development and validation of a nomogram for pneumonia risk in burn patients with inhalation injury: A multicenter retrospective cohort study".

Comment on "Development and validation of a nomogram for pneumonia risk in burn patients with inhalation injury: A multicenter retrospective cohort study".